Radiographic imaging for medical purposes is well known in the art. Radiographic images of the chest, for example, provide important diagnostic information for detecting and treating a large number of medical conditions involving the lungs, bony structures in the chest, the upper abdominal organs, the vascular structures of the lungs, and the disc spaces of the mid-thoracic spine.
Because of the great advantages provided by digital images, radiographs are increasingly stored and manipulated in digital form. Digital radiographs may be created either by direct capture of the original image in digital form, or by conversion of an image acquired by an “analog” system to digital form. Digital images simplify record keeping, such as in matching radiographs to the correct patient, and allow for more efficient storage and distribution. Digital images also allow for digital correction and enhancement of radiographs, and for application of computer-aided diagnostics and treatment.
Radiologists are highly skilled in interpreting radiographic images, but limitations of radiographic systems, and variability between systems, can hamper proper interpretation. Sources of variability related to the acquisition of radiographic images may include the spatial sampling of the images; the gray scale resolution (or “bit depth”); the Modulation Transfer Function (“MTF”) of the system; image contrast; and noise.
The sampling function of an image can generally be expressed as the number of pixels in a unit length. Generally, sampling is performed at or near the Nyquist rate to avoid aliasing. For example, a highly-detailed chest radiogram may have 5,000 pixels per inch, for a minimum discernable feature size of 200 microns. A uniform spatial resolution between images can be important in automated systems, such as when software is used to identify or analyze features having specific spatial characteristics in a radiogram.
Bit depth is the number of data bits used to store the brightness value of each pixel of an image. Different radiographic systems may produce radiograms with different bit depths. For example, bit depths commonly range from 10 to 12 bits. Bit depth is important not just in respect to the quality of the original image, but becomes a limiting factor when digitally manipulating images, such as when processing the images to accentuate particular features or in computer-aided diagnosis. Insufficient bit depth can result in degraded processed images, imaging artifacts, and unreliable diagnostic results.
Modulation Transfer Function (MTF) is the spatial frequency response of an imaging system, or of an imaging component. High spatial frequencies correspond to fine image detail, while low spatial frequencies correspond to larger structures. The contrast produced on a radiographic image by features of different sizes may differ due to the system MTF. Typically, the contrast of features at a high spatial frequency can be reduced relative to the contrast of features at a low spatial frequency due to the limited resolving power of the imaging instrument. Because of the reduction in amplitude variation of smaller features due to MTF, the visibility of smaller features in a radiograph may be masked by overlying larger structures in the image.
Contrast involves the brightness differences between neighboring pixels in an image. Contrast concerns not just the absolute difference between the brightest and darkest pixels, but also the brightness distribution of the intermediate pixels. For example, the distribution of brightness values may be skewed towards the bright or dark end of the distribution range, making it difficult to discern features having similar brightness. For both the human observer and for automated systems, it is beneficial that different radiographic images have substantially similar contrast to ensure consistent interpretation during reading or processing.
One technique used to correct for differences in gray scale appearance is known as histogram matching. A histogram is essentially a bar graph representation of the distribution of pixel values in an image, in which the heights of the bars are proportional to the number of pixels in the image having that pixel value. As is known in the art, histogram matching is a pixel mapping derived from an input cumulative density function (CDF) and a target CDF. As CDFs are monotonic and lie in the 0-1 range, histogram matching is a simple matter of alignment, or matching, the two CDFs. Histogram matching is generally used in areas where it is of interest to be able to directly compare pixels values of similar scenes; it is a global technique in that it uses pixel values and not spatial information in anyway. It does not, for example, address the problem localized contrast differences in images of the same scene.
Noise is a universal limitation of all measurement systems, including radiographic systems. In radiographic systems, it is generally necessary to limit the cumulative exposure of a subject to x-rays; a tradeoff for short exposure times is an increase in noise on the resulting image. Noise on radiographic images tends to primarily manifest themselves at higher spatial frequencies. At the highest spatial frequencies, noise may predominate, limiting the ability to discern detail in an image.
Both to improve the uniformity of radiographic images for interpretation by radiologists and other professionals, and to provide a good foundation for subsequent digital processing and analysis of the images, a principled system for normalization of images may be desirable. The normalization process may account for differences between images at difference spatial frequencies and may address the problems of contrast and noise.